Learning patterns of university student retention

被引:37
|
作者
Nandeshwar, Ashutosh [1 ]
Menzies, Tim [2 ]
Nelson, Adam [2 ]
机构
[1] Kent State Univ, Kent, OH 44242 USA
[2] W Virginia Univ, Morgantown, WV 26505 USA
关键词
Data mining; Student retention; Predictive modeling; Financial aid; HIGHER EDUCATION; DROPOUTS; MODEL;
D O I
10.1016/j.eswa.2011.05.048
中图分类号
TP18 [人工智能理论];
学科分类号
081104 ; 0812 ; 0835 ; 1405 ;
摘要
Learning predictors for student retention is very difficult. After reviewing the literature, it is evident that there is considerable room for improvement in the current state of the art. As shown in this paper, improvements are possible if we (a) explore a wide range of learning methods; (b) take care when selecting attributes; (c) assess the efficacy of the learned theory not just by its median performance, but also by the variance in that performance; (d) study the delta of student factors between those who stay and those who are retained. Using these techniques, for the goal of predicting if students will remain for the first three years of an undergraduate degree, the following factors were found to be informative: family background and family's social-economic status, high school GPA and test scores. (C) 2011 Elsevier Ltd. All rights reserved.
引用
收藏
页码:14984 / 14996
页数:13
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